Automatically generating a personalized digest of meetings

ABSTRACT

Automatically generating a meeting digest of a set of meetings is provided. A set of topics of interest to the parties to the set of meetings is detected utilizing a user model associated with a user that is based on at least one of communications, relationships, and roles of the parties to the set of meetings. Topic-related content associated with the set of topics of interest to the parties is extracted from meeting data corresponding to the set of meetings. Then, the meeting digest of the set of meetings is generated using the topic-related content associated with the set of topics of interest to the parties extracted from the meeting data corresponding to the set of meetings.

The present application is related to U.S. patent application Ser. No.12/915,584 entitled “AUTOMATIC STATIC VIDEO SUMMARIZATION” filed on Oct.29, 2010 and assigned to the assignee of the present application, thedetails of which are incorporated herein by reference.

BACKGROUND

1. Field

The disclosure relates generally to a computer implemented method,computer system, and computer program product for automaticallygenerating a meeting digest of a set of one or more meetings that ispersonalized to a particular user based on a user model associated withthat particular user.

2. Description of the Related Art

Meetings are an important venue within an enterprise for exchanginginformation. The capture of important points, decisions, and/or actionswithin the exchange of information during the meetings is oftenperformed informally and manually. With the increase of geographicallydispersed teams, enterprises are adopting technology to hold thesemeetings remotely via the World Wide Web. Commercial web-conferencingsystems are being widely used by today's global enterprises to reducethe cost and time expenditures associated with travel. Many of thesecurrent web-conferencing systems facilitate automatic capture ofinformation by recording remotely held meetings in their entirety andmaking the recording available for later viewing. These recordings aremade available after the fact as a video and tend to last sixty minutesor more. These types of recordings can support people who did not attendthe meeting, but hope to catch up on what information they may havemissed. The recordings also are helpful to those individuals thatattended the meeting, but do not recall specifics of the meeting or arelooking for particular facts that were disseminated during the meeting(e.g. a product release date).

SUMMARY

According to one embodiment of the present invention, a computerimplemented method for automatically generating a meeting digest of aset of meetings is provided. A computer detects a set of topics ofinterest to parties to the set of meetings utilizing a user modelassociated with a user that is based on at least one of communications,relationships, and roles of the parties to the set of meetings. Thecomputer extracts topic-related content associated with the set oftopics of interest to the parties from meeting data corresponding to theset of meetings. Then, the computer generates the meeting digest of theset of meetings using the topic-related content associated with the setof topics of interest to the parties extracted from the meeting datacorresponding to the set of meetings. In other embodiments of thepresent invention, a computer system and a computer program product forautomatically generating a meeting digest of a set of meetings areprovided.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 is a pictorial representation of a network of data processingsystems in which illustrative embodiments may be implemented;

FIG. 2 is a diagram of a data processing system in which illustrativeembodiments may be implemented;

FIG. 3 is a diagram illustrating an example of a meeting digest systemin accordance with an illustrative embodiment;

FIGS. 4A-4C are a specific example of a digest email in accordance withan illustrative embodiment;

FIG. 5 is a flowchart illustrating a process for generating and sendinga digest email in accordance with an illustrative embodiment;

FIG. 6 is a flowchart illustrating a process for generating a user modelin accordance with an illustrative embodiment; and

FIG. 7 is a flowchart illustrating a process for generating a meetingdigest of a set of meetings in accordance with an illustrativeembodiment.

DETAILED DESCRIPTION

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method, or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.), or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module,” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described below with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

With reference now to the figures, and in particular, with reference toFIGS. 1-3, diagrams of data processing environments are provided inwhich illustrative embodiments may be implemented. It should beappreciated that FIGS. 1-3 are only meant as examples and are notintended to assert or imply any limitation with regard to theenvironments in which different embodiments may be implemented. Manymodifications to the depicted environments may be made.

FIG. 1 depicts a pictorial representation of a network of dataprocessing systems in which illustrative embodiments may be implemented.Network data processing system 100 is a network of computers and otherdevices in which the illustrative embodiments may be implemented.Network data processing system 100 contains network 102, which is themedium used to provide communications links between the computers andthe other various devices connected together within network dataprocessing system 100. Network 102 may include connections, such aswire, wireless communication links, or fiber optic cables.

In the depicted example, server 104 and server 106 connect to network102, along with storage unit 108. Server 104 may be, for example, aconferencing server device with high speed connections to network 102.Server 104 may reside, for example, within a web-conferencing systemthat provides web-conferencing services to a plurality of clientdevices. Also, server 104 may represent a plurality of web-conferencingservers. Server 106 may be, for example, a meeting digest server devicethat provides digests of meeting summaries. The meeting summaries maybe, for example, summaries of meetings that were conducted via aweb-conferencing system utilizing server 104. Thus in this example, ameeting refers to parties, such as individuals or persons, utilizingcomputing devices connected via network 102, which come together onlineto discuss topics of interest. However, it should be noted that ameeting also may be conducted locally in a physical location or may beconducted both remotely online and locally. Further, server 106 may alsorepresent a plurality of server devices.

Storage unit 108 is a network storage device capable of storing data ina structured or unstructured format. Storage unit 108 may be, forexample, a network storage device that provides storage for meetingsummaries, user models associated with a plurality of users, and digestsof a set of one or more meeting summaries that are personalized tospecific users based on the user models. The user models includepreferences and interests of each respective user. Further, it should benoted that storage unit 108 may store other data, such as, for example,user information that may include user identification and passwords usedto access the web-conferencing system.

Clients 110, 112, and 114 also connect to network 102. Clients 110, 112,and 114 are clients to server 104 and server 106. In the depictedexample, server 104 and server 106 may provide information, such as bootfiles, operating system images, and applications to clients 110, 112,and 114. In this example, clients 110, 112, and 114 are data processingsystems, such as, for example, personal computers, network computers,laptop computers, handheld computers, personal digital assistants, smartphones, cellular phones, or any combination thereof. Users may utilizeclients 110, 112, and 114 to receive and view meeting digest informationprovided by, for example, server 106. Server 106 may send the meetingdigest information to one or more of clients 110, 112, and 114 in, forexample, an email or as a calendar entry. Also, server 106 may make themeeting digest accessible via a Webpage. Furthermore, it should be notedthat network data processing system 100 may include additional serverdevices, client devices, and other devices not shown.

Program code located in network data processing system 100 may be storedon a computer recordable storage medium and downloaded to a computer orother device for use. For example, program code may be stored on acomputer recordable storage medium on server 106 and downloaded toclient 110 over network 102 for use on client 110.

In the depicted example, network data processing system 100 is theInternet with network 102 representing a worldwide collection ofnetworks and gateways that use the Transmission ControlProtocol/Internet Protocol (TCP/IP) suite of protocols to communicatewith one another. At the heart of the Internet is a backbone ofhigh-speed data communication lines between major nodes or hostcomputers, consisting of thousands of commercial, governmental,educational, and other computer systems that route data and messages. Ofcourse, network data processing system 100 also may be implemented as anumber of different types of networks, such as for example, an intranet,a local area network (LAN), or a wide area network (WAN). FIG. 1 isintended as an example, and not as an architectural limitation for thedifferent illustrative embodiments.

With reference now to FIG. 2, a diagram of a data processing system isdepicted in accordance with an illustrative embodiment. Data processingsystem 200 is an example of a computer, such as server 106 in FIG. 1, inwhich computer readable program code or instructions implementingprocesses of illustrative embodiments may be located. In thisillustrative example, data processing system 200 includes communicationsfabric 202, which provides communications between processor unit 204,memory 206, persistent storage 208, communications unit 210,input/output (I/O) unit 212, and display 214.

Processor unit 204 serves to execute instructions for softwareapplications or programs that may be loaded into memory 206. Processorunit 204 may be a set of one or more processors or may be amulti-processor core, depending on the particular implementation.Further, processor unit 204 may be implemented using one or moreheterogeneous processor systems, in which a main processor is presentwith secondary processors on a single chip. As another illustrativeexample, processor unit 204 may be a symmetric multi-processor systemcontaining multiple processors of the same type.

Memory 206 and persistent storage 208 are examples of storage devices216. A storage device is any piece of hardware that is capable ofstoring information, such as, for example, without limitation, data,computer readable program code in functional form, and/or other suitableinformation either on a transient basis and/or a persistent basis.Memory 206, in these examples, may be, for example, a random accessmemory, or any other suitable volatile or non-volatile storage device.Persistent storage 208 may take various forms, depending on theparticular implementation. For example, persistent storage 208 maycontain one or more devices. For example, persistent storage 208 may bea hard drive, a flash memory, a rewritable optical disk, a rewritablemagnetic tape, or some combination of the above. The media used bypersistent storage 208 may be removable. For example, a removable harddrive may be used for persistent storage 208.

In this example, persistent storage 208 stores meeting summaries 218,user models 220, and meeting digests 222. Meeting summaries 218represent a set of one or more meeting summaries that are generated by ameeting digest server, such as server 106 in FIG. 1, from meeting datathat are captured by a conferencing server, such as server 104 inFIG. 1. The captured meeting data may include, for example, textualdata, such as speech to text transcriptions, documents, and annotations;visual data, such as video clips, slide data, picture data, andgraphical data; and audio data.

The meeting digest server may generate meeting summaries 218 by using,for example, a text-based analysis and/or a video-based analysis of thecaptured meeting data. The text-based analysis and video-based analysisof the captured meeting data are used to extract selected text and videosegments to generate a summary of a meeting. The text-based analysis mayinclude, for example, natural language processing and the video-basedanalysis may include, for example, automated scene detection.

User models 220 represent a set of one or more user models. Each usermodel in user models 220 is associated with a particular user andincludes user preferences and interests 224 associated with eachparticular user. The meeting digest server generates the user model fromdata collected from a plurality of sources regarding a particular user.The data sources may be, for example, a user's electronic calendar on apersonal computer, the user's browser history of websites visited, theuser's electronic address book that may provide relationship informationwith other users, the user's email communications, the user's instantmessaging communications, the user's chat room discussions, the user'sIP telephony discussions, documents created or viewed by the user on thepersonal computer, video clips viewed by the user, a meeting programthat may provide information regarding the user's participation and/orthe user's role in the meeting, such as a moderator, organizer, orpresenter, or any other sources of information regarding the user thatmay be used to determine user preferences and interests 224.

Meeting digests 222 represent a set of one or more meeting digests. Ameeting digest includes a set of one or more meeting summaries that ispersonalized to a particular user. A meeting digest may includekeywords, selected video frames, snippets from email communications,snippets from instant messaging communications, snippets from chat roomdiscussions, extracted audio content, speech to text phrases, extracted“to do” items from an electronic “to do” list, extracted project datesfrom an electronic calendar, or any other information that may be ofinterest to a particular user. The meeting digest server personalizesthe set of one or more meeting summaries in the meeting digest to aparticular user by utilizing the user model associated with thatparticular user that includes the user's preferences and interests.Further, the meeting digest server may insert hyperlinks within the oneor more personalized meeting summaries. A hyperlink is an active linkthat references or automatically directs the user to data associatedwith the personalized meeting summaries when activated by an input, suchas a mouse click over the hyperlink.

Communications unit 210, in this example, provides for communicationwith other data processing systems or devices. In this example,communications unit 210 is a network interface card. Communications unit210 may provide communications through the use of either or bothphysical and wireless communications links.

Input/output unit 212 allows for the input and output of data with otherdevices that may be connected to data processing system 200. Forexample, input/output unit 212 may provide a connection for user inputthrough a keyboard, a mouse, and/or some other suitable input device.Further, input/output unit 212 may send output to a printer. Display 214provides a mechanism to display information to a user.

Instructions for the operating system, applications, and/or programs maybe located in storage devices 216, which are in communication withprocessor unit 204 through communications fabric 202. In thisillustrative example, the instructions are in a functional form onpersistent storage 208. These instructions may be loaded into memory 206for running by processor unit 204. The processes of the differentembodiments may be performed by processor unit 204 using computerimplemented instructions, which may be located in a memory, such asmemory 206. These instructions are referred to as program code, computerusable program code, or computer readable program code that may be readand run by processor unit 204. The program code, in the differentembodiments, may be embodied on different physical or computer readablestorage media, such as memory 206 or persistent storage 208.

Program code 226 is located in a functional form on computer readablemedia 228 that is selectively removable and may be loaded onto ortransferred to data processing system 200 for running by processor unit204. Program code 226 and computer readable media 228 form computerprogram product 230. In one example, computer readable media 228 may becomputer readable storage media 232 or computer readable signal media234. Computer readable storage media 232 may include, for example, anoptical or magnetic disc that is inserted or placed into a drive orother device that is part of persistent storage 208 for transfer onto astorage device, such as a hard drive, that is part of persistent storage208. Computer readable storage media 232 also may take the form of apersistent storage, such as a hard drive, a thumb drive, or a flashmemory that is connected to data processing system 200. In someinstances, computer readable storage media 232 may not be removable fromdata processing system 200.

Alternatively, program code 226 may be transferred to data processingsystem 200 using computer readable signal media 234. Computer readablesignal media 234 may be, for example, a propagated data signalcontaining program code 226. For example, computer readable signal media234 may be an electro-magnetic signal, an optical signal, and/or anyother suitable type of signal. These signals may be transmitted overcommunication links, such as wireless communication links, an opticalfiber cable, a coaxial cable, a wire, and/or any other suitable type ofcommunications link. In other words, the communications link and/or theconnection may be physical or wireless in the illustrative examples. Thecomputer readable media also may take the form of non-tangible media,such as communication links or wireless transmissions containing theprogram code.

In some illustrative embodiments, program code 226 may be downloadedover a network to persistent storage 208 from another device or dataprocessing system through computer readable signal media 234 for usewithin data processing system 200. For instance, program code stored ina computer readable storage media in a server data processing system maybe downloaded over a network from the server to data processing system200. The data processing system providing program code 226 may be aserver computer, a client computer, or some other device capable ofstoring and transmitting program code 226.

The different components illustrated for data processing system 200 arenot meant to provide architectural limitations to the manner in whichdifferent embodiments may be implemented. The different illustrativeembodiments may be implemented in a data processing system includingcomponents in addition to, or in place of, those illustrated for dataprocessing system 200. Other components shown in FIG. 2 can be variedfrom the illustrative examples shown. The different embodiments may beimplemented using any hardware device or system capable of executingprogram code. As one example, data processing system 200 may includeorganic components integrated with inorganic components and/or may becomprised entirely of organic components excluding a human being. Forexample, a storage device may be comprised of an organic semiconductor.

As another example, a storage device in data processing system 200 isany hardware apparatus that may store data. Memory 206, persistentstorage 208, and computer readable media 228 are examples of storagedevices in a tangible form.

In another example, a bus system may be used to implement communicationsfabric 202 and may be comprised of one or more buses, such as a systembus or an input/output bus. Of course, the bus system may be implementedusing any suitable type of architecture that provides for a transfer ofdata between different components or devices attached to the bus system.Additionally, a communications unit may include one or more devices usedto transmit and receive data, such as a modem or a network adapter.Further, a memory may be, for example, memory 206 or a cache such asfound in an interface and memory controller hub that may be present incommunications fabric 202.

During the course of developing illustrative embodiments it wasdiscovered that web-conferencing creates the possibility of usingrecordings of meetings as archives of information that was disseminatedin the meetings. As the number of repositories of such recorded meetingsgrows, the value and utility of these data stores will depend onproviding tools that will help a user quickly browse, find, and retrievedata of interest to that user. Given the high time cost involved withviewing a recorded meeting in its entirety, a need exists for tools thatassist efficient data discovery and access. Currently, it is too timeconsuming and inefficient to sift through a large number of recordedmeetings to find specific information or to refresh one's memory on whatexactly was discussed in past meetings.

To facilitate access to recorded meetings and encourage improvedinformation sharing among users, illustrative embodiments provide ameeting analytics system that automatically generates personalizedmeeting digests. Illustrative embodiments generate these meeting digestsfrom content of existing meeting artifacts, such as meeting videorecordings, presentation slides, and human annotations of the content(e.g., tags). Illustrative embodiments then directly share these meetingdigests with meeting attendees through, for example, emails or calendarupdates.

Illustrative embodiments may automatically generate these topic-basedmeeting digests on, for example, a daily, weekly, or monthly basis.Alternatively, illustrative embodiments may generate a meeting digestafter each meeting or on demand. If a meeting digest includes multiplemeeting summaries, the meeting digest may be organized by, for example,meeting dates, main topics of the meetings, or by other meetingmetadata, such as meetings belonging to a same series of meetings. Asummary of each meeting in the digest may include, for example, a visualsummary of the meeting's videos (e.g., thumbnails of the top N-number ofkey frames), a keyword summary of the textual content automaticallyderived from meeting transcripts and presentation slides, and extractedkey meeting artifacts, such as decision points, “to do” items, dates,names, and uniform resource locators (URLs). Illustrative embodimentsalso automatically hyperlink extracted meeting artifacts tocorresponding video segments stored in the meeting repository such thatwith a simple mouse click, the corresponding video segments can beplayed back directly via a video player.

Moreover, if there is more than one meeting summary in a digest,illustrative embodiments automatically generate a topic-basedinteractive visual summary of all the meetings, which may include topiccontent changes within the meetings over time. In other words,illustrative embodiments automatically generate a single summary foreach meeting and a combined digest of several meeting summaries, whichmay possibly include a variety of different topics with different peoplepresenting material on the different topics. Also, illustrativeembodiments may visualize “to do” items, which are related to recurringmeetings associated with a project, on a timeline to track the progressof that project. Within the same framework, illustrative embodiments mayalso create activities, which may be generically thought of asemail-based reminders of tasks assigned to a person and trackedcentrally, based on human-provided annotations, such as those itemsspecifically tagged as task items. Furthermore, illustrative embodimentsmay generate meeting digest feeds to news aggregators for dissemination.Illustrative embodiments may generate the meeting digest feeds in, forexample, an extensible markup language (XML) format for consumption byany XML data feed consumer.

Most importantly, illustrative embodiments customize or personalize thecontent of the meeting digest based on a user's interests andpreferences. For example, users may explicitly specify what topics theyare interested in and how much detail is to be included within a meetingdigest. In addition, illustrative embodiments may automatically mine auser's interests and preferences based on past meetings the user hasattended and past meeting artifacts the user has accessed. Illustrativeembodiments may also mine user interests and preferences from externaldata sources, such as, for example, emails, chat messages, and socialactivities. Illustrative embodiments generate a user model associatedwith the user from the data extracted from the various data sourceswhich captures the user's general interests and preferences.

In addition, illustrative embodiments may customize the content of ameeting digest based on the results of a search query. For example,illustrative embodiments may rank all meetings according to eachmeeting's relevance to the search query and then generate a meetingdigest that summarizes a pre-determined number of top ranking meetingsretrieved. Illustrative embodiments adjust or edit the length of ameeting summary and the details included in the meeting summary based ona user's preferences and interests found in a user model associated withthe user.

Thus, illustrative embodiments of the present invention provide acomputer implemented method, computer system, and computer programproduct for automatically generating a meeting digest of a set ofmeetings. A set of meetings is one or more different meetings. Acomputer detects a set of one or more topics of interest to parties tothe set of meetings utilizing a user model associated with a user thatis based on at least one of communications, relationships, and roles ofthe parties to the set of meetings. The computer extracts topic-relatedcontent associated with the set of topics of interest to the partiesfrom meeting data corresponding to the set of meetings. Then, thecomputer generates the meeting digest of the set of meetings using thetopic-related content associated with the set of topics of interest tothe parties extracted from the meeting data corresponding to the set ofmeetings. Afterward, the computer generates an email that includes themeeting digest of the set of meetings and sends the email via a networkto the user associated with the user model.

With reference now to FIG. 3, a diagram illustrating an example of ameeting digest system is depicted in accordance with an illustrativeembodiment. Meeting digest system 300 may be implemented in network dataprocessing system 100 in FIG. 1, for example. Illustrative embodimentsutilize meeting digest system 300 to generate meeting digests of a setof one or more meetings that are personalized to a particular user basedon a user model associated with the user.

Meeting digest system 300 includes conferencing server device 302,meeting digest server device 304, and client device 306. Conferencingserver device 302 may be, for example, server 104 in FIG. 1.Conferencing server device 302 provides conferencing services to aplurality of client devices. Conferencing server device 302 may combine,for example, telephonic-based communications and/or computer-basedcommunications using either or both wireless and wired connections.Conferencing server device 302 captures the contents of an entiremeeting as a record of that meeting and then stores the recording of themeeting in meeting records 308. Meeting records 308 is a set of one ormore meeting recordings. It should be noted that conferencing serverdevice 302 may record a plurality of different meeting at a same time.

Conferencing server device 302 may send meeting recordings 308 tomeeting digest server device 304 on a pre-determined time intervalbasis, such as daily, weekly, or monthly. Alternatively, conferencingserver device 302 may send a recording of a meeting to meeting digestserver device 304 at the completion of each meeting or on demand bymeeting digest server device 304. Upon receiving meeting recordings 308,meeting digest server device 304 generates meetings summarization 310.

Meetings summarization 310 includes a summarization of each meetingwithin meeting recordings 308. Meeting summary 312 is an example of onesuch summarization of a meeting. Meeting summary 312 may include, forexample, a summary of text, such as textual summary 314, a staticsummary of video clips or slides, such as visual summary 316, and asummary of keywords, such as keyword summary 318. However, it should benoted that meeting summary 312 may include any relevant summaryinformation. In addition, meeting summary 312 may include more or lessinformation than illustrated in this example.

Meeting digest server device 304 generates meetings summary 312 usingtextual analysis 320 and visual analysis 322 of a recording of ameeting, such as meeting recordings 308. Textual analysis 320 may usenatural language processing (NLP), for example. Natural languageprocessing (NLP) is a field of computer science, machine learning, andlinguistics concerned with interactions between computers and human(natural) languages. Specifically, natural language processing is theprocess of a computer extracting meaningful information from naturallanguage input. Natural language processing algorithms are grounded inmachine learning, especially statistical machine learning. Some examplesof tasks performed by natural language processing are: 1) automaticsummarization, which produces a readable summary of a chunk of text; 2)named entity recognition, which determines which items within a chunk oftext are proper names, such as people, places, and enterprises; 3)relationship extraction, which identifies relationships among namedentities within a chunk of text; 4) topic segmentation and recognition,which separates chunks of text into segments, each of which is devotedto a topic, and identifies the topic of the segment.

Visual analysis 322 may use automated scene detection, for example. Theautomated scene detection detects frames within a video clip having acorrelation or similarity with one another. Then, visual analysis 322clusters the detected frames into different clusters based on theircorrelation with one another. In addition, visual analysis 322 ranks theclusters of frames based on, for example, relevance to a topic of themeeting. At least a portion of the frames are selected based on clusterranking for inclusion in a static summary of the video clip, such asvisual summary 316. The static summary is generated by combiningthumbnail images of the selected frames.

Meeting digest server device 304 includes database 324, which is arepository for meeting data and user data. Database 324 may be, forexample, persistent storage 208 in FIG. 2. It should be noted that eventhough database 324 is illustrated as being located within meetingdigest server device 304, in an alternative illustrative embodimentdatabase 324 may be located remotely as a separate storage device, suchas storage 108 in FIG. 1.

Database 324 stores each meeting summary in meeting summaries 326.Further, database 324 may store meeting summaries 326 in an extensiblemarkup language format, for example. Meeting summaries 326 may be, forexample, meeting summaries 218 in FIG. 2. Database 324 also stores usermodels 328 and meeting digests 330. User models 328 and meeting digests330 may be, for example, user models 220 and meeting digests 222 in FIG.2.

User models 328 represent a set of one or more user models. Each usermodel in user models 328 is associated with a particular user. Inaddition, each user model in user models 328 includes user preferencesand interests 332. User preferences and interests 332 are associatedwith each particular user that corresponds to a particular user model inuser models 328. Meeting digest server device 304 may generate aparticular user model by collecting data regarding that particular userfrom a plurality of data sources. A data source may be, for example, auser's electronic calendar on a personal computer, such as user calendar334 on client device 306. Other data sources may be, for example, theuser's browser history, the user's electronic address book and/orcontact list, the user's electronic communications, the user's IPtelephony discussions, data stored on the user's computer, or any othersource of information regarding the user that may be used to determineuser preferences and interests 332.

Meeting digests 330 represent a set of one or more meeting digests. Eachmeeting digest in meeting digests 330 includes a set of one or moremeeting summaries that are personalized to a particular user based onuser preferences and interests 332 within a particular user model ofuser models 328 that corresponds to that particular user.

Meeting digest server device 304 sends email with meeting digestincluded 336 to client device 306. Upon accessing email with meetingdigest included 336, a user of client device 306 views meeting digestwith a personalized set of meeting summaries 338. Meeting digest with apersonalized set of meeting summaries 338 is a customized set of one ormore meeting summaries included in a meeting digest that arepersonalized to that particular user.

The contents of meeting digest with a personalized set of meetingsummaries 338 may include, for example, a static visual summary of ameeting's video clips; a keyword summary of textual content of themeeting; extracted key meeting artifacts, such as decision points, “todo” items, dates, names, and URLs; a topic flow visualization of themeeting's content; and a visualization of “to do” items on a timeline.Furthermore, all meeting artifacts included within meeting digest with apersonalized set of meeting summaries 338 may be hyperlinked tocorresponding visual data segments or textual data segments of themeeting.

Also, the amount of detail and the amount of information that ishighlighted within meeting digest with a personalized set of meetingsummaries 338 depends on the level of relevance of that information tothe user model associated with that particular user, which may take intoaccount the user's relations within the user's social network. Meetingdigest server device 304 may use, for example, a list of participants ofa meeting that a user attended, a hierarchical structure of employees inan enterprise that the user works for, an electronic address book orbuddy list of the user, and/or social media contacts of the user todetermine relations within the user's social network.

An example of how meeting digest server device 304 may use the socialand organizational relations in personalizing a meeting summary may beas follows: John and Mary have an IP telephony discussion aboutproteins. Mary is the head of the organization, so John determines thatthe topic of proteins must be particularly important to theorganization. Later, John participates in a meeting where Bob, the headof marketing, and Mary are presenting, along with Jerry, a moderator whointroduces Bob and Mary. Mary's portion of the discussion includes onesection about DNA and another about proteins. Bob then discussesinformation about proteins. Subsequently, John receives a personalizedmeeting digest of the meeting that includes a lot of detail regardingMary's discussion of proteins, less detail regarding Bob's discussion ofproteins, while Jerry's portion of the meeting is completely removed.Thus, the prior IP telephony communication about a particular topic(i.e., proteins) or an organizational relationship between parties(i.e., Mary is the head of the organization that John is a part of andBob is the head of marketing) is used to vary the level of detail andthe highlighted information within the personalized meeting digest.

Thus, meeting digest server device 304 performs automated visual andtextual analysis on captured meeting content to generate visual andtextual summaries of meetings. Meeting digest server device 304 mayperform automated scene detection, frame clustering, and cluster rankingto generate a static visual summary of video clips of a meeting. Meetingdigest server device 304 may also utilize natural language processingtechniques, such as topic modeling or named-entity detection, to groupmeetings by topics and to identify important names of people,organizations, or projects found within meeting transcripts or slides.Furthermore, meeting digest server device 304 may use rule-basedapproaches to detect important dates, action items, and decision points.

As a result, meeting digest server device 304 focuses on summarizingmeetings that a user may have attended and provides the most relevantinformation from these meetings to the user. Meeting digest serverdevice 304 leverages social network relations to provide a bettersummarization of possibly multiple meetings in one meeting digestdocument, which is hyperlinked to video playbacks and meeting-relateddocuments. Meetings are complex events that involve participation ofseveral people, as well as attachment of several documents, which havetime and location information associated with these documents. A seriesof meetings may exist where some of the meetings are follow-up meetings,or predecessor meetings of other meetings, or meetings may turn intoregularly scheduled meetings. Meeting digest server device 304 may useall of this information to model users' interests, activities, andsocial networks.

With reference now to FIGS. 4A-4C, a specific example of a digest emailis depicted in accordance with an illustrative embodiment. Digest email400 is an email that includes a meeting digest, such as email withmeeting digest included 336 in FIG. 3. The meeting digest includes apersonalized set of one or more meeting summaries, such as meetingdigest with a personalized set of meeting summaries 338 in FIG. 3.

For example, digest email 400 includes personalized meeting summary 402,personalized meeting summary 404, and extracted relevant metadata 406.Personalized meeting summary 402 and personalized meeting summary 404are summaries of two different meetings, which were attended by aparticular user, that are customized to that particular user utilizingthe user's preferences and interests included in a user model associatedwith that particular user. Both personalized meeting summary 402 andpersonalized meeting summary 404 include textual summary 408, visualsummary 410, and keyword summary 412, such as textual summary 314,visual summary 316, and keyword summary 318 in FIG. 3. Also,personalized meeting summary 402 and personalized meeting summary 404include highlighted hyperlinks 414, which are active links tocorresponding textual and visual data within meeting recordings, such asmeeting recordings 308 in FIG. 3.

Extracted relevant metadata 406 include “to-do” items 416, names 418,dates 420, and uniform resource locators 422. “To-do” items 416 includesitems that need to be performed, such as, for example, set up aninternal server, finish query page development, and send an email to theorganizer. Names 418 may include, for example, names of people mentionedin a meeting discussion, such as John Smith, but were not included in alist of participants of the meeting. Dates 420 include dates of tasksthat need to be performed, such as, for example, user interface designsare due next week and a demonstration of the user interface is due onJan. 12, 2012. Uniform resource locators 422 may include, for example,the uniform resource locator of a company's library of documents, suchas www.company.com/library. It should be noted that digest email 400 isonly intended as an example and not intended as a limitation onillustrative embodiments. In other words, illustrative embodiments mayinclude less information that what is shown in the example of digestemail 400 or may include other information not shown.

With reference now to FIG. 5, a flowchart illustrating a process forgenerating and sending a digest email is shown in accordance with anillustrative embodiment. The process shown in FIG. 5 may be implementedin a data processing system, such as, for example, data processingsystem 200 in FIG. 2.

The process begins when the data processing system generates a usermodel associated with a user based on at least one of communications,relationships, and roles of parties to a set of meetings (step 502). Theuser model may be, for example, a user model within user models 328 inFIG. 3. Then, the data processing system detects a set of topics ofinterest to the parties using the generated user model associated withthe user (step 504). The data processing system may detect the set oftopics of interest to the parties by utilizing user preferences andinterests located within each user model, such as user preferences andinterests 332 in FIG. 3. Afterward, the data processing system extractstopic-related content associated with the detected set of topics ofinterest to the parties from captured meeting data corresponding to theset of meetings, such as meeting recordings 308 in FIG. 3 (step 506).The data processing system extracts the topic-related content associatedwith the detected set of topics of interest to the parties from thecaptured meeting data corresponding to the set of meetings by performingat least one of video analysis, textual analysis, automated scenedetection, and natural language processing of the captured meeting data.For example, the data processing system may use textual analysis 320 andvisual analysis 322 in FIG. 3 to extract the topic-related content.

Subsequently, the data processing system generates a meeting digest ofthe set of meetings using the topic-related content associated with thedetected set of topics of interest to the parties extracted from thecaptured meeting data corresponding to the set of meetings (step 508).In addition, the data processing system generates an email that includesthe generated meeting digest of the set of meetings, such as email withmeeting digest included 336 in FIG. 3 (step 510). Then, the dataprocessing system sends the email that includes the generated meetingdigest of the set of meetings to the user associated with the generateduser model via a network, such as network 102 in FIG. 1 (step 512).Further, the data processing system modifies the generated meetingdigest of the set of meetings based on user input (step 514). Moreover,the data processing system modifies the generated user model associatedwith the user based on user feedback (step 516). The user feedback mayinclude, for example, selections of content by the user within the emailthat includes the generated meeting digest of the set of meetings. Theprocess terminates thereafter.

With reference now to FIG. 6, a flowchart illustrating a process forgenerating a user model is shown in accordance with an illustrativeembodiment. The process shown in FIG. 6 may be implemented in a dataprocessing system, such as, for example, data processing system 200 inFIG. 2. Also, the process shown in FIG. 6 may be implemented in step 502in FIG. 5.

The process begins when the data processing system collects dataregarding a user from a plurality of sources via a network, such asnetwork 102 in FIG. 1 (step 602). In addition, the data processingsystem generates a statistical model based on the collected dataregarding the user (step 604). Then, the data processing systemdetermines preferences and interests of the user based on thestatistical model (step 606).

Afterward, the data processing system generates a user model associatedwith the user that includes the preferences and interests of the user,such as one of user models 328 that includes user preferences andinterests 332 in FIG. 3 (step 608). Subsequently, the data processingsystem edits a set of meeting summaries corresponding to a set ofmeetings based on the preferences and interests of the user included inthe generated user model to personalize the set of meeting summaries tothe user (step 610). Then, the data processing system generates ameeting digest of the set of meetings to include the personalized set ofmeeting summaries corresponding to the set of meetings, such as meetingdigest with a personalized set of meeting summaries 338 in FIG. 3 (step612). The personalized set of meeting summaries includes hyperlinks tomeeting data associated with the personalized set of meeting summaries.The hyperlinks may be, for example, highlighted hyperlinks 414 in FIG.4.

In addition, the data processing system sends the generated meetingdigest of the set of meetings including the personalized set of meetingsummaries to the user associated with the generated user model via thenetwork (step 614). The data processing system may send the generateddigest of the personalized set of meeting summaries in, for example, anemail, such as email with meeting digest included 336 in FIG. 3, or asan entry in a user calendar, such as user calendar 334 in FIG. 3. Also,the data processing system receives user feedback through selection ofone or more of the hyperlinks to the meeting data located within thepersonalized set of meeting summaries in the generated digest (step616). Furthermore, the data processing system updates the statisticalmodel based on the received user feedback (step 618). Thereafter, theprocess returns to step 602 where the data processing system continuesto collect data regarding the user.

With reference now to FIG. 7, a flowchart illustrating a process forgenerating a meeting digest of a set of meetings is shown in accordancewith an illustrative embodiment. The process shown in FIG. 7 may beimplemented in a data processing system, such as, for example, dataprocessing system 200 in FIG. 2. Also, the process shown in FIG. 7 maybe implemented in step 508 in FIG. 5.

The process begins when the data processing system receives capturedmeeting data corresponding to a set of meetings, such as meetingrecordings 308 in FIG. 3 (step 702). The data processing system mayreceive the captured meeting data corresponding to the set of meetingsin response to a request from the data processing system to a serverdevice, such as conferencing server device 302 in FIG. 3, for thecaptured meeting data corresponding to the set of meetings.Alternatively, the data processing system may receive the capturedmeeting data corresponding to the set of meetings from the conferencingserver device automatically on a predetermined time interval basis, suchas daily, weekly, or monthly, or after the recording of each meeting iscompleted.

After receiving the captured meeting data corresponding to a set ofmeetings in step 702, the data processing system receives a search queryregarding the captured meeting data corresponding to the set of meetings(step 704). The data processing system generates a summary of eachmeeting in the set of meetings using a text-based analysis and/or avideo-based analysis of the captured meeting data corresponding to theset of meetings (step 706). For example, meeting digest server device304 may generate meeting summary 312 using textual analysis 320 andvisual analysis 322 in FIG. 3. In addition, the data processing systemretrieves a user model that includes user preferences and interests froma database. For example, meeting digest server device 304 retrieves oneof user models 328 that includes user preferences and interests 332 fromdatabase 324 in FIG. 3 (step 708).

Further, the data processing system edits each generated summary of eachmeeting in the set of meetings based on the user preferences andinterests located within the retrieved user model to personalize eachgenerated summary to the user (step 710). Then, the data processingsystem assigns a weight to each personalized summary of each meeting inthe set of meetings based on the user preferences and interests locatedwithin the retrieved user model (step 712). For example, the dataprocessing system may assign a personalized summary of meeting A with alower weight than a personalized summary of meeting B, which has ahigher weight than a personalized summary of meeting C, which has alower weight than the personalized summary of meeting A. In other words,the weight of meeting A<the weight of meeting B>the weight of meetingC<the weight of meeting A. Subsequently, the data processing systemgenerates a meeting digest of the set of meetings to include a rankedlist of personalized summaries of each meeting in the set of meetingsbased on the assigned weight of each personalized summary (step 714).For example, using the illustration above, the meeting digest of the setof meetings would be the personalized summary of meeting B first, andthen the personalized summary of meeting A, followed by the personalizedsummary of meeting C. It should be noted that the data processing systemtakes into consideration the social relationships of the user during theweighting and ranking of the personalized summaries. The processterminates thereafter.

Thus, illustrative embodiments of the present invention provide acomputer implemented method, computer system, and computer programproduct for automatically generating a meeting digest of a set of one ormore meetings that is personalized to a particular user based on a usermodel associated with the user. The descriptions of the variousembodiments of the present invention have been presented for purposes ofillustration, but are not intended to be exhaustive or limited to theembodiments disclosed. Many modifications and variations will beapparent to those of ordinary skill in the art without departing fromthe scope and spirit of the described embodiment. The terminology usedherein was chosen to best explain the principles of the embodiment, thepractical application or technical improvement over technologies foundin the marketplace, or to enable others of ordinary skill in the art tounderstand the embodiments disclosed here.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

What is claimed is:
 1. A computer implemented method for automaticallygenerating a meeting digest of a set of meetings, the computerimplemented method comprising: detecting, by a computer, a set of topicsof interest to parties to the set of meetings utilizing a user modelassociated with a user that is based on at least one of communications,relationships, and roles of the parties to the set of meetings;receiving, by the computer, recorded meeting data corresponding to theset of meetings from a conferencing server that recorded contents of theset of meetings; extracting, by the computer, topic-related contentassociated with the set of topics of interest to the parties from therecorded meeting data corresponding to the set of meetings received fromthe conferencing server; and generating, by the computer, the meetingdigest of the set of meetings using the topic-related content associatedwith the set of topics of interest to the parties extracted from therecorded meeting data corresponding to the set of meetings.
 2. Thecomputer implemented method of claim 1, further comprising: generating,by the computer, an email that includes the meeting digest of the set ofmeetings; and sending, by the computer, the email that includes themeeting digest of the set of meetings via a network.
 3. The computerimplemented method of claim 2, further comprising: modifying, by thecomputer, the user model associated with the user based on receivinguser feedback through selection of one or more highlighted hyperlinkswithin the email that includes the meeting digest of the set ofmeetings, wherein the highlighted hyperlinks are active links tocorresponding content within the recorded meeting data received from theconferencing server.
 4. The computer implemented method of claim 1,further comprising: modifying, by the computer, the meeting digest ofthe set of meetings based on user input.
 5. The computer implementedmethod of claim 1, further comprising: mining, by the computer, dataregarding the user from a plurality of data sources that includes anelectronic calendar corresponding to the user on a personal computer, abrowser history corresponding to the user on the personal computer, anelectronic address book corresponding to the user on the personalcomputer, email communications corresponding to the user on the personalcomputer, instant messaging communications corresponding to the user onthe personal computer, chat room discussions corresponding to the useron the personal computer, internet protocol telephony discussionscorresponding to the user on the personal computer, documents created bythe user on the personal computer, documents viewed by the user on thepersonal computer, video clips viewed by the user on the personalcomputer, and meeting programs on the personal computer providinginformation regarding at least one of participation of the user inmeetings or a role of the user in meetings; generating, by the computer,a statistical model based on the data mined from the plurality ofsources regarding the user; determining, by the computer, preferencesand interests of the user based on the statistical model generated fromthe data mined from the plurality of sources regarding the user; andgenerating, by the computer, the user model associated with the user toinclude the preferences and interests of the user.
 6. The computerimplemented method of claim 5, further comprising: editing, by thecomputer, a set of meeting summaries corresponding to the set ofmeetings based on the preferences and interests of the user locatedwithin the user model to personalize the set of meeting summaries to theuser, wherein the set of meeting summaries includes a set of thumbnailframes as a visual summary of recorded videos of the set of meetings, aset of keyword summaries of textual content extracted from transcriptsand presentation slides of the set of meetings, a set of extractedmeeting artifacts of the set of meetings that includes action items,dates, names, and uniform resource locators, and a set of highlightedhyperlinks that are active links to corresponding content within therecorded meeting data corresponding to the set of meetings; andgenerating, by the computer, the meeting digest of the set of meetingsto include the personalized set of meeting summaries corresponding tothe set of meetings.
 7. The computer implemented method of claim 1,further comprising: receiving, by the computer, a search query regardingthe recorded meeting data corresponding to the set of meetings; andgenerating, by the computer, a summary of each meeting in the set ofmeetings recorded by the conferencing server using at least one of atext-based analysis and a video-based analysis of the recorded meetingdata corresponding to the set of meetings received from the conferencingserver.
 8. The computer implemented method of claim 7, furthercomprising: editing, by the computer, the summary of each meeting in theset of meetings recorded by the conferencing server based on userpreferences and interests located within the user model to personalizethe summary to the user; assigning, by the computer, a weight to thepersonalized summary of each meeting in the set of meetings based on theuser preferences and interests located within the user model; andgenerating, by the computer, the meeting digest of the set of meetingsto include a ranked list of personalized summaries of each meeting inthe set of meetings based on the assigned weight of each personalizedsummary.
 9. The computer implemented method of claim 1, wherein themeeting digest of the set of meetings includes at least one of keywords,selected video frames, snippets from email communications, snippets frominstant messaging communications, snippets from chat room discussions,extracted audio content, speech to text extracted phrases, and extracted“to do” items associated with the recorded meeting data corresponding tothe set of meetings.
 10. The computer implemented method of claim 1,wherein the computer extracts the topic-related content from the set oftopics of interest to the parties by performing at least one of videoanalysis, text analysis, automated scene detection, and natural languageprocessing of the recorded meeting data corresponding to the set ofmeetings received from the conferencing server.
 11. The computerimplemented method of claim 10, wherein the automated scene detectionincludes detecting frames within a video recording of a meeting having acorrelation with other frames, clustering the frames into differentclusters of frames based on frame correlation, ranking the differentclusters of frames based on relevance to a topic of the meeting,selecting a set of frames based on the ranking of the different clustersof frames, and generating a static visual summary of a set of thumbnailimages of the video recording of the meeting based on the selected setof frames, and wherein the natural language processing of the meetingdata corresponding to the set of meetings includes at least one of topicdetection and named entity detection within the meeting data.
 12. Acomputer system for automatically generating a meeting digest of a setof meetings, the computer system comprising: a bus system; a storagedevice connected to bus system, wherein the storage device storescomputer readable program code; and a processor connected to the bussystem, wherein the processor executes the computer readable programcode to detect a set of topics of interest to parties to the set ofmeetings utilizing a user model associated with a user that is based onat least one of communications, relationships, and roles of the partiesto the set of meetings; receive recorded meeting data corresponding tothe set of meetings from a conferencing server that recorded contents ofthe set of meetings; extract topic-related content associated with theset of topics of interest to the parties from the recorded meeting datacorresponding to the set of meetings received from the conferencingserver; and generate the meeting digest of the set of meetings using thetopic-related content associated with the set of topics of interest tothe parties extracted from the recorded meeting data corresponding tothe set of meetings.
 13. The computer system of claim 12, wherein theprocessor further executes the computer readable program code togenerate an email that includes the meeting digest of the set ofmeetings; and send the email that includes the meeting digest of the setof meetings via a network.
 14. A computer program product stored on anon-transitory computer readable medium having computer readable programcode embodied thereon that is executable by a computer for automaticallygenerating a meeting digest of a set of meetings, the computer programproduct comprising: computer readable program code for detecting a setof topics of interest to parties to the set of meetings utilizing a usermodel associated with a user that is based on at least one ofcommunications, relationships, and roles of the parties to the set ofmeetings; computer readable program code for receiving recorded meetingdata corresponding to the set of meetings from a conferencing serverthat recorded contents of the set of meetings; computer readable programcode for extracting topic-related content associated with the set oftopics of interest to the parties from the recorded meeting datacorresponding to the set of meetings received from the conferencingserver; and computer readable program code for generating the meetingdigest of the set of meetings using the topic-related content associatedwith the set of topics of interest to the parties extracted from therecorded meeting data corresponding to the set of meetings.
 15. Thecomputer program product of claim 14, further comprising: computerreadable program code for generating an email that includes the meetingdigest of the set of meetings; and computer readable program code forsending the email that includes the meeting digest of the set ofmeetings via a network.
 16. The computer program product of claim 15,further comprising: computer readable program code for modifying theuser model associated with the user based on receiving user feedbackthrough selection of one or more highlighted hyperlinks within the emailthat includes the meeting digest of the set of meetings, wherein thehighlighted hyperlinks are active links to corresponding content withinthe recorded meeting data received from the conferencing server.
 17. Thecomputer program product of claim 14, further comprising: computerreadable program code for modifying the meeting digest of the set ofmeetings based on user input.
 18. The computer program product of claim14, further comprising: computer readable program code for mining dataregarding the user from a plurality of data sources that includes anelectronic calendar corresponding to the user on a personal computer, abrowser history corresponding to the user on the personal computer, anelectronic address book corresponding to the user on the personalcomputer, email communications corresponding to the user on the personalcomputer, instant messaging communications corresponding to the user onthe personal computer, chat room discussions corresponding to the useron the personal computer, internet protocol telephony discussionscorresponding to the user on the personal computer, documents created bythe user on the personal computer, documents viewed by the user on thepersonal computer, video clips viewed by the user on the personalcomputer, and meeting programs on the personal computer providinginformation regarding at least one of participation of the user inmeetings or a role of the user in meetings; computer readable programcode for generating a statistical model based on collected the datamined from the plurality of sources regarding the user; computerreadable program code for determining preferences and interests of theuser based on the statistical model generated from the data mined fromthe plurality of sources regarding the user; and computer readableprogram code for generating the user model associated with the user toinclude the preferences and interests of the user.
 19. The computerprogram product of claim 18, further comprising: computer readableprogram code for editing a set of meeting summaries corresponding to theset of meetings based on the preferences and interests of the userlocated within the user model to personalize the set of meetingsummaries to the user, wherein the set of meeting summaries includes aset of thumbnail frames as a visual summary of recorded videos of theset of meetings, a set of keyword summaries of textual content extractedfrom transcripts and presentation slides of the set of meetings, a setof extracted meeting artifacts of the set of meetings that includesaction items, dates, names, and uniform resource locators, and a set ofhighlighted hyperlinks that are active links to corresponding contentwithin the recorded meeting data corresponding to the set of meetings;and computer readable program code for generating the meeting digest ofthe set of meetings to include the personalized set of meeting summariescorresponding to the set of meetings.
 20. The computer program productof claim 14, further comprising: computer readable program code forreceiving a search query regarding the recorded meeting datacorresponding to the set of meetings; and computer readable program codefor generating a summary of each meeting in the set of meetings recordedby the conferencing server using at least one of a text-based analysisand a video-based analysis of the recorded meeting data corresponding tothe set of meetings received from the conferencing server.